To select the anchor users, we adopt a greedy method to maximize the coverage of local models, instead of random selection as in [1]}. We define an unweighted undirected graph \(\mathcal {G} = (\mathcal {U}, E)\)  of users, where the edges represent the relationships between the users. As the weight \(\mathcal {W}\)  directly affects the performance of our model, we assign an edge between two users only when the similarity between them is within the kernel bandwidth \(k_{h_W}\) , i.e., edge \(E_{ij} = 1\)  if \(K_{h_W} (dist (i, j)) > 0\) , otherwise \(E_{ij} = 0\) .
